^{1}M.S student, Department of Civil Engineering, University of Sistan and Baluchestan, zahedan, Iran

^{2}Assistant Professor, Department of Civil Engineering, University of Sistan and Baluchestan, zahedan, Iran

^{3}Assistant Professor, Department of Civil Engineering, University of Alzahra, Tehran, Iran

Abstract

A reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. In this study, a new approach based on the Adaptive Neural Fuzzy Inference System (ANFIS) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. These parameters contain Web width, Effective depth, Shear span to depth ratio, Concrete compressive strength, Main reinforcement ratio, Horizontal shear reinforcement ratio and Vertical shear reinforcement ratio. The ANFIS model is developed based on 214 experimental database obtained from the literature. The data used in the present study, out of the total data, 80% was used for training the model and 20% for checking to validate the model. The results indicated that ANFIS is an effective method for predicting the shear strength of reinforced concrete (RC) deep beams and has better accuracy and simplicity compared to the empirical methods.

[1] BarraBirrcher, D.. (2009). "Design of reinforced concrete deep beams for strength and serviceability". Ph. D Thesis, The University of Texas at Austin, USA, PP. 370.

[2] Collins,MP., Kuchma, D.. (1999). "How safe are our large, lightly reinforced concrete beams, slab and footing". ACI Struct J, Vol. 96, pp. 482-490.

[3] Kani, GNJ.. (1967)."How safe are our large RC beams" ACI journal Proceedings, Vol. 64,.pp. 128-41.

[4] Yang,KH., Ashour, AF.. (2011). " Strut-and-tie model based on crack band theory for deep beams". J Struct Eng, Vol. 137,pp. 1030–1038.

[5] Pal, M., Deswal,S.. (2011)."Support vector regression based shear strength modeling of deep beams". Comput Struct, Vol. 89, pp. 1430–1439.

[6] Ashour,AF., Alvarez, LF., Toropov, VV.. (2003). "Empirical modeling of shear strength of RC deep beams by genetic programming". ComputStruct, Vol. 81, pp. 331–338.

[7] Zararis,PD..(2003)."Shear compression failure in reinforced concrete deep beams" J StructEng, Vol. 129, pp. 544–553.

[8] ACI-318.(1995)."Building Code Requirements for Reinforced Concrete (ACI 318 M-95) and Commentary".ACI 318RM-95, American Concrete Institute.

[9] ACI-318,318-05/318 R-05.(2005)."Building Code Requirements for Structural Concrete and Commentary". American Concrete Institute, pp. 432.

[10] ACI-318, 318-08.(2008)."Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute".

[11] ACI-318, 318-11.(2011)."Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute".

[12] CSA. (1994). "Design of Concrete Structures: A National Standard of Canada". CAN-A23.3-94,Toronto.

[13] Matamoros, AB., Wong KH..(2003). Design of simply supported deep beams using strut-and-tie models". ACI Struct J, Vol. 100, pp. 704–712.

[14] Park,JW., Kuchma, D.. (2007). " Strut-and-tie model analysis for strength prediction of deep beams". ACI StructJ, Vol. 104, pp. 657–666.

[15] Goh, ATC..(1995)."Prediction of ultimate shear strength of deep beams using neural networks".ACI Struct J, Vol. 92, pp. 28-32.

[16] Yeh,IC..(1998)."concrete strength with augment-neuron networks".J. Mater. Civ. Eng., Vol. 10, pp. 263–268.

[17] Atici, U..(2011)."Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network".Expert Systems with Applications, Vol. 38, pp. 9609–9618.

[18] Oreta,AWC., Kawashima, K..(2003). Neural network modeling of concrete compressive strength and strain of circular concrete columns".J. Struct. Eng., Vol. 129, pp. 554-561.

[19] Sanad, A.,Saka, M..(2001)."Prediction of ultimate shear strength of reinforced- concrete deep beams using neural networks". J. Struct. Eng., Vol. 127, pp. 818–828.

[20] Lee,SC..(2003)."Prediction of concrete strength using artificial neural networks". Eng. Struct, Vol. 25, pp. 849–857.

[21] Kim,JI., Kim, DK., Feng, MQ. And Yazdani F..(2004)."Application of neural networks for estimation of concrete strength".J. Mater. Civ. Eng., Vol. 16, pp. 257–264.

[22] Mansour, MY.,Dicleli, M., Lee, JY. and Zhang J.. (2004)."Predicting the shear strength of reinforced concrete beams using artificial neural networks". Eng. Struct, Vol. 26, pp. 781–799.

[23] Cladera, A., Mari,AR..(2004)."Shear design procedure for reinforced normal and high strength concrete beams using artificial neural networks. PartII: beams with stirrups".Eng. Struct, Vol. 26, pp. 927–936.

[24] Tang, CW.. (2006)."Using radial basis function neural networks to model torsional strength of reinforced concrete beams".Comput Concr, Vol. 3, pp. 335–355.

[25] Abdalla,JA.,Elsanosi, A. and Abdelwahab A..(2007)."Modeling and simulation of shear resistance of RC beams using artificial neural network".J. Franklin Inst, Vol. 344, pp. 741–756.

[26] Caglar, N., Elmas, M., Yaman, ZD. andSaribiyik M..(2008)."Neural network in 3-dimensional dynamic analysis of reinforced concrete buildings". Constr. Build. Mater, Vol. 22, pp. 788–800.

[27] Arslan, MH.. (2010)."Predicting of tensional strength of RC beams by using different artificial neural network algorithms and building codes". Adv. Eng. Softw,Vol. 41, pp. 946–955.

[28] Ashour,AF., Alvarez, LF., Toropov, VV.. (2003)."Empirical modeling of shear strength of RC deep beams by genetic programming".Comput Struct, Vol. 81, pp. 331–338.

[29] Gandomi,AH.,Alavi, AH., Yun, GJ.. (2011). "Nonlinear modeling of shear strength of SFRC beams using linear genetic programming".Struct. Eng. Mech, Vol. 38, pp, 1–25.

[30] Gandomi,AH., Yong, GJ., Alavi, AH..(2013)."An evolutionary approach for modeling of shear strength of RC deep beams". Materials and Structures, Vol. 46, pp. 2109-2119.

[32] Abudlkudir, A., Ahmet, T. and Murat Y.. (2006)."Prediction of Concrete Elastic Modulus Using Adaptive Neuro-Fuzzy Inference System" Journal of Civil Engineering and Environmental Systems, Vol. 23, pp. 295–309.

[33] Tesfamariam, S.,Najjaran, H..(2007). "Adaptive Network-Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design". Journal of Materials in Civil Engineering, Vol. 19, pp. 550-560.

[34] Fonseca, E., Vellasco, S. and Andrade,S..(2008). "A Neuro-Fuzzy Evaluation of Steel Beams Patch Load Behaviour". Journal of Advances in Engineering Software, Vol. 39, pp. 535-555.

[35] Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat,MZ.,Jameel, M., Hakim SJS.andZargar M..(2013)."Application of the ANFIS model in deflection prediction of concrete deep beam".Structural Engineering and Mechanics, Vol. 45, pp. 319-332.

[36] Smith, KN., Vantsiotis, AS..(1982). "Shear-strength of deep beams". J. Am. Concr. Inst, Vol. 79, pp. 201–213.

[37] Kong,FK., Robins, PJ., Cole, DF..(1970). "Web reinforcement effects on deep beams".ACI Journal Proceeding, Vol. 67, pp. 1010–1017.

[38] Clark, AP..(1951)."Diagonal tension in reinforced concrete beams". ACI Journal Proceeding, Vol. 48, pp. 145–156.

[39] Oh,JK., Shin, SW..(2001). "Shear strength of reinforced high-strength concrete deep beams". ACI Struct J, Vol. 98, pp. 164–173.

[40] Aguilar, G., Matamoros, AB., Parra-Montesinos,GJ., Ramirez, JA.and Wight JK..(2002)."Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams". ACI Struct J, Vol. 99, pp. 539–548.

[41] Quintero-Febres,CG., Parra-Montesinos, G., Wight, JK.. (2006)."Strength of struts in deep concrete members designed using strut-and-tie method". ACI Struct J, Vol. 103, pp. 577–586.

[42] Tan,KH., Kong, FK., Teng, S. and Guan L.. (1995). "High-strength concrete deep beams with effective span and shear span variations".ACI Struct J, Vol. 92, pp. 395–405.

[43] Anderson,NS., Ramirez, JA.. (1989). "Detailing of stirrup reinforcement". ACI Struct J, Vol. 86, pp. 507–515.

[44] Sugeno, M..(1985). "Industrial Applications of Fuzzy Control", Elsevier, Amsterdam.

[45] Golbraikh, A. and Tropsha, A.. (2002). "Beware of q2!".J. Mol. Graphics Modell, Vol. 20, pp. 269-276.

[46] Roy, PP., Roy,K..(2008)."On some aspects of variable selection for partial least squares regression models".QSAR Comb.Sci, Vol. 27, pp. 302-313.